Spatial transcriptomics: a bibliometric analysis with large language model on English literatures

TL;DR

This paper analyzes trends in spatial transcriptomics research using bibliometric methods and large language models, highlighting its growing role in cancer and immunology.

Contribution

The study introduces the use of large language models to enhance bibliometric analysis in spatial transcriptomics research.

Findings

ST publications increased significantly, with 500 papers published in 2023.

Top journals in ST research are predominantly from the Nature Publishing Group.

Keywords like 'tumor microenvironment' and 'immune infiltration' indicate ST's expanding role in cancer and immunology.

Abstract

Spatial transcriptomics (ST) integrates spatial data with transcriptomic information, providing high-resolution maps of gene expression within tissue contexts. It has revolutionized studies on cellular function and disease mechanisms, particularly in cancer and immunology. We conducted a bibliometric analysis of 1197 publications from the Web of Science (2015–24), focusing on publication trends, journal distribution, and keyword analysis to identify key research areas in ST. ST publications surged from 2021, with 500 papers in 2023. Five of the top 10 journals are from the Nature Publishing Group. Keyword analysis identified emerging trends like “tumor microenvironment,” “immune infiltration,” and “biomarker,” highlighting ST’s expanding role in cancer and immunology. International collaboration among multidisciplinary teams is crucial for maximizing ST’s potential, and understanding its trends will guide its future impact. Large language models can further enrich the results of bibliometric research, making the findings of bibliometrics more comprehensive and specific.

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